CN116700212A - Fault diagnosis system edge end construction method based on distributed Internet of things - Google Patents
Fault diagnosis system edge end construction method based on distributed Internet of things Download PDFInfo
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Abstract
The invention relates to a fault diagnosis system edge end construction method based on a distributed Internet of things, which comprises the following steps: the method comprises the steps that data acquisition is carried out on equipment objects based on a factory method mode through a data acquisition module, and acquired data are transmitted to an edge end; after the data is subjected to standardized processing through the data processing module, the data is cleaned and subjected to data aggregation processing; analyzing and standardizing the data into a row protocol through a data analysis module, and storing the row protocol into a time sequence database; the processed data is uploaded to the cloud through an MQTT gateway module through an MQTT protocol; and fifthly, performing fault diagnosis analysis on the received data through a fault diagnosis system intelligent analysis module, making a decision, and returning a decision result to the edge end. The invention can greatly reduce the influence caused by multi-source heterogeneous data and different protocols in the fault diagnosis process; the universality of the fault diagnosis system is greatly improved, and the fault diagnosis system can be rapidly deployed when a diagnosis object is changed without making great adjustment on the system.
Description
Technical Field
The invention belongs to the technical field of fault diagnosis of mechanical equipment, and particularly relates to a fault diagnosis system edge end construction method based on a distributed Internet of things.
Background
The industrial Internet of things is characterized in that data acquired by various sensors and controllers are transmitted to a cloud computing center platform, and then data processing and the like are performed. The internet of things information system is utilized to carry out data operation and other operations on the whole manufacturing supply and some important information used, so that efficient production and supply can be achieved, further, the transition of industrial manufacturing from automation to intellectualization is completed, and the progress of the existing manufacturing industry to intelligent manufacturing is overcome by high standards and high criteria.
Along with the continuous improvement of the quality and complexity of mechanical equipment, the intelligent design and production requirements of equipment ends are more strict, and simultaneously, the reliability and stability of electromechanical equipment are also more required. When some parts of the mechanical equipment fail due to some accidents, if the equipment is not diagnosed and corrected in time, the equipment cannot only be damaged by the faults, but also the whole production chain can be paralyzed. Fault diagnosis techniques for mechanical equipment are therefore of increasing interest.
The existing fault diagnosis technology mainly utilizes different state information of sensor acquisition equipment to acquire, such as temperature, pressure, vibration amplitude and the like, and then processes, analyzes and makes diagnosis decision on the acquired state information. The traditional fault diagnosis method comprises an intelligent expert diagnosis method based on shallow knowledge and deep knowledge, and the like. The existing fault diagnosis method mainly comprises the following technical defects:
1. the diversity of network protocols and the heterogeneous data sources of multiple sources.
In fault diagnosis systems, network transmission has wired or wireless multi-protocol characteristics, and the diversity of network protocols and heterogeneous data sources also bring great challenges to the interactive functions and stable and efficient operation of the diagnosis systems.
2. The fault diagnosis edge deployment time is long.
In the existing environment, the construction of a fault diagnosis system is carried out aiming at a certain device, and the time spent in the construction stage of the bottom layer of the system is long from the deployment, collection and data transmission of a sensor to the deployment and fault diagnosis identification of an edge end algorithm model, so that the construction speed of the fault diagnosis system is greatly prolonged.
3. The mobility of the fault diagnosis edge-end construction method is extremely poor.
The existing fault diagnosis system is often constructed and designed for a specific device, has weak universality, and once the aimed object is changed, great adjustment on the diagnosis system is often needed. For example, the machine tool fault diagnosis and prediction method and system based on edge calculation and cloud cooperation proposed in patent CN109933004 are not applicable any more once the diagnosis object is changed from the original machine tool to other equipment.
Disclosure of Invention
The invention aims to provide a distributed Internet of things-based fault diagnosis system edge end construction method, which adopts a modularized service architecture to normalize multi-source heterogeneous data, defines a standardized MQTT protocol at the same time, greatly improves the equipment interactivity of a fault diagnosis system, deploys an intelligent analysis module at a cloud end to process various fault signals and the like, and can enable the edge end to have extremely strong universality and mobility.
The invention provides a fault diagnosis system edge end construction method based on a distributed Internet of things, which comprises the following steps:
step one, data acquisition is carried out on equipment objects based on a factory method mode through a data acquisition module, and acquired data are transmitted to an edge end through respective defined communication protocols and data formats; the device object includes a sensor, a controller;
step two, after the data is subjected to standardized processing through a data processing module, the data is cleaned and subjected to data aggregation processing;
analyzing and standardizing the data into a row protocol by a data analysis module, and storing the row protocol into a time sequence database;
step four, the processed data is uploaded to a cloud end through an MQTT gateway module through an MQTT protocol;
and fifthly, performing fault diagnosis analysis on the received data through a fault diagnosis system intelligent analysis module, making a decision, and returning a decision result to the edge end.
Further, the data acquisition module is specifically configured to:
collecting vibration data of a vibration sensor according to a specified frequency;
collecting temperature sensor data according to a specified frequency;
simultaneously connected with a plurality of sensors;
synchronizing time between hosts using a network time protocol, inserting a time stamp into the collected data, and synchronizing with a clock;
serial interface data is collected.
Further, the data collection of the device object based on the factory method mode in the step one includes:
defining abstract product class, namely Sensor interface, which is used for describing the attribute and function of the Sensor, checking the state of the Sensor and initializing the Sensor by connecting with the Sensor, and obtaining Sensor data;
dividing sensor subclasses according to different sensor types and different parameters, checking sensor states in the sensor subclasses, and initializing and collecting data;
defining abstract factory classes for creating different types of sensor instances, namely specific factory classes, according to different parameter requirements; the abstract factory class is packaged with a method for creating different types of devices;
when the sensor data are collected, a method for creating different types of equipment in a specific factory class is called, and a sensor instance is returned to collect the sensor data.
Further, the third step includes:
defining a data type, including the name of the data and the data type;
defining a data format, including a data length and a unit;
defining a data structure comprising attribute information of data, and defining a data acquisition module comprising a plurality of sensors or controllers, wherein acquired data formats comprise vibration, temperature and pressure attributes;
determining a mapping rule, mapping original data into a standardized format according to a mapping data relationship, and mapping sensor data into a row protocol type;
mapping between data models is carried out, and sensor data are converted into a row protocol format and stored in a time sequence database.
Further, the fourth step includes:
1) The MQTT gateway module runs an MQTT client and is connected with a cloud MQTT proxy server at the same time, and edge information is in butt joint with the cloud, wherein the edge information comprises an edge equipment ID and a type;
2) The MQTT gateway module distributes the data after secondary processing and the original data to a specific theme;
3) The MQTT proxy server of the cloud end is used as a subscriber to subscribe to a specific topic.
Further, the fifth step includes:
verifying the function of an intelligent analysis module of the fault diagnosis system based on offline data, if the function is normal, establishing communication connection, and performing test verification based on data uploaded by the MQTT gateway; if the verification is correct, deploying the intelligent analysis module of the fault diagnosis system at the edge end, constructing the edge end of the fault diagnosis system and connecting the edge end with the sensor, and carrying out overall verification and debugging.
Further, the fifth step further includes:
and setting an API (application program interface) in the intelligent analysis module of the fault diagnosis system, and introducing a new algorithm and a corresponding data file, and debugging after the new algorithm and the corresponding data file are introduced.
By the scheme, the method for constructing the edge of the fault diagnosis system based on the distributed Internet of things can greatly reduce the influence caused by multi-source heterogeneous data and different protocols in the fault diagnosis process; the universality of the fault diagnosis system is greatly improved, and the fault diagnosis system can be rapidly deployed when a diagnosis object is changed without making great adjustment on the system.
The foregoing description is only an overview of the present invention, and is intended to provide a better understanding of the present invention, as it is embodied in the following description, with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
FIG. 1 is a flow chart of a method for constructing an edge of a fault diagnosis system according to an embodiment of the present invention;
FIG. 2 is a flow chart of data collection using a factory method mode in accordance with an embodiment of the invention;
FIG. 3 is a flow diagram of multi-source heterogeneous data processing in accordance with one embodiment of the present invention;
FIG. 4 is a schematic layout diagram of each module in the method for constructing the edge of the fault diagnosis system of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Referring to fig. 1 to 4, the present embodiment provides a method for constructing an edge of a fault diagnosis system based on a distributed internet of things, including the following steps:
step one: and the data acquisition module is used for acquiring data of equipment objects (various sensors, controllers and the like) based on a factory method mode, and the acquired data are transmitted to the edge end through a communication protocol and a data format which are defined respectively.
In a specific example, the data acquisition module acquires data of a sensor and the like, and the functions mainly comprise: 1. vibration data acquisition capability, namely acquiring vibration data of the vibration sensor according to a designated frequency; 2. temperature data acquisition capability, which is to acquire temperature sensor data according to a designated frequency; 3. the multi-device acquisition capability, each data acquisition module can support to be connected with a plurality of sensors and other devices at the same time; 4. data timestamp marking, using Network Time Protocol (NTP) to synchronize time between hosts, inserting timestamps into collected data, and synchronizing with a clock; 5. the collected data comprises data such as a serial interface.
Referring to fig. 2, the process of data acquisition using the factory method mode is as follows:
1. an abstract product class, i.e. Sensor interface, is defined for describing the properties and functions of the Sensor, i.e. interfacing with the Sensor, checking the status of the Sensor and initializing, obtaining Sensor data, etc.
2. The specific product class, i.e. the sensor subclasses, e.g. TEMTsensor, VIBsensor, etc., is implemented according to different sensor types and different parameters, and the method of checking the sensor status and initializing and collecting data is implemented in the sensor subclasses.
3. An abstract factory class sensor is defined, which can create different types of sensor instances according to different parameter requirements, namely a specific factory class, such as a vibsenorfactor and the like, wherein the sensor contains a method of creating different types of devices, namely a create_sensor, which can create specific sensor objects according to the type and the ID of the input sensor and the like.
4. When the system is running, when the pressure sensor data is collected, a method 'create_sensor' in the VIBSensorFactoy can be called, so that an example 'VIBSensor' is returned, and the collection of the pressure sensor data is realized.
The method adopts a factory method mode to collect data, and the creation logic of the sensor object is encapsulated into a sensor factor class, so that the flexibility and the expandability of codes are greatly improved, for example, when a humidity sensor needs to be created, the method for creating the object of the factory class can be directly called, and corresponding equipment types, equipment IDs and the like are transmitted, so that a new humidity sensor object is created.
Step two: and after the data is subjected to standardized processing through the data processing module, performing operations such as data cleaning, data aggregation and the like. The data cleaning is to perform secondary processing on the standardized data, remove invalid data, normalize and the like, and the data aggregation comprises the steps of aggregating the data of a plurality of data sources to form unified data summarization; facilitating further operations.
Step three: and analyzing and standardizing the data into a row protocol by a data analysis module, and storing the row protocol into a time sequence database. Comprising the following steps:
1. defining a data type: i.e. the name of the data, the data type, etc. For a vibration sensor, the data type is defined as 'VIB', for a temperature sensor, the data type is defined as 'TEMP', and the data formats are all floating point numbers.
2. A data format is defined, including information of data length, units, etc., and for vibration sensors, units are defined as mm.
3. A data structure is defined, including information such as attributes of data, and for a data acquisition module, a plurality of sensors or controllers are defined, and acquired data formats include attributes such as vibration, temperature, pressure and the like.
4. The mapping rules are determined, in particular, the mapping may be performed using the following rules: measurement: a character string type; tag keys, character string type; tag values: a character string type; field keys: string type, field values: floating point number type; timetable: UNIX timestamp. According to the mapping data relationship, the original data is mapped into a standardized format, the sensor data is mapped into a row protocol type and the like.
5. Mapping between data models is realized, and sensor data are converted into a row protocol format and stored in a time sequence database.
Specifically, taking rolling bearing fault diagnosis as an example, data collected by the vibration sensor can be standardized into a row protocol according to the mapping rule, as follows: rolling bearing, id=001, sensor=vib01 vibration value= 0.37 1679669117000, where ID is the rolling bearing number, VIB01 is the vibration sensor number, vibration value is the vibration value detected by the sensor, and the unit is mm;16179669117000 is a UNIX timestamp, accurate to milliseconds; for multi-sensor detection, the row protocol can be standardized as follows: ringing sensing, id=001, sensor1=vib01, sensor2=temp 01 registration value=0.37, temp= 56 1679669117000 where TEMP01 is the temperature sensor number and 56 is the value acquired by the sensor in degrees celsius.
Specifically, for standardized row protocols, the HTTP API is used to write data into the timing database, send POST requests to the/write endpoint, and provide the required row protocol in the request body.
Step four: and uploading the processed data to the cloud through an MQTT gateway module through an MQTT protocol. (MQTT protocol standardization)
Firstly, designing an MQTT message structure, namely selecting to use a publish/subscribe model; secondly, after the conversion of Modbus and other protocol data into a row protocol format is realized, mapping between data models is constructed, namely, the original data format is converted into a new attribute, such as the conversion of Modbus protocol vibration data into MQTT protocol vibration attribute; and simultaneously configuring an MQTT gateway, uploading data in a time sequence database to a cloud end through an MQTT protocol, and encrypting the data by adopting a TLS protocol in the uploading process. The method specifically comprises the steps of 1, registering, running an MQTT client in an MQTT gateway module, connecting with a cloud MQTT proxy server (browser) and butting edge information with the cloud, wherein the information comprises an edge equipment ID, a type and the like; 2. the method comprises the steps that a publishing theme (Topic), an MQTT gateway module publishes (public) data after secondary processing and primary data to a specific theme, and publishes vibration data acquired by a vibration sensor to a VIB Topic, and publishes temperature data acquired by a temperature sensor to a TEMP Topic; 3. subscription (subscore) topics, the MQTT proxy server in the cloud will serve as a subscriber (subscore) to subscribe to specific topics, such as VIB Topic, TEMP Topic, etc.
Step five: and carrying out fault diagnosis analysis on the received data through an intelligent analysis module of the fault diagnosis system, making a decision, and returning a decision result to the edge.
The intelligent diagnosis module is designed based on the system and the requirements of the fault algorithm, when the intelligent diagnosis module is deployed, debugging and verification are needed, the function of the intelligent analysis module of the fault diagnosis system is verified based on offline data, if the function is normal, communication connection can be established, and test verification is carried out based on the data uploaded by the MQTT gateway; if the verification is correct, the intelligent analysis module of the fault diagnosis system can be deployed at the edge end, the edge end of the fault diagnosis system and the connection with a sensor and the like are built, and overall verification and debugging are carried out; meanwhile, an API is designed in the intelligent analysis module of the fault diagnosis system and is used for importing a new algorithm, a corresponding data file and the like, and debugging is carried out after the new algorithm and the corresponding data file are imported. By means of the expandability of the fault diagnosis module, the system supports the operation of various fault diagnosis algorithms, can realize the utility evaluation of different fault algorithms on various fault types, thereby realizing the lower utility evaluation and real-time operation functions of the fault diagnosis algorithms, and can complete the functions of introducing new algorithms and the like in the operation process, and the system has extremely strong expandability.
By the method for constructing the edge of the fault diagnosis system based on the distributed Internet of things, the influence caused by multi-source heterogeneous data and different protocols can be greatly reduced in the fault diagnosis process; the universality of the fault diagnosis system is greatly improved, and the fault diagnosis system can be rapidly deployed when a diagnosis object is changed without making great adjustment on the system.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and it should be noted that it is possible for those skilled in the art to make several improvements and modifications without departing from the technical principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.
Claims (7)
1. The method for constructing the edge end of the fault diagnosis system based on the distributed Internet of things is characterized by comprising the following steps:
step one, data acquisition is carried out on equipment objects based on a factory method mode through a data acquisition module, and acquired data are transmitted to an edge end through respective defined communication protocols and data formats; the device object includes a sensor, a controller;
step two, after the data is subjected to standardized processing through a data processing module, the data is cleaned and subjected to data aggregation processing;
analyzing and standardizing the data into a row protocol by a data analysis module, and storing the row protocol into a time sequence database;
step four, the processed data is uploaded to a cloud end through an MQTT gateway module through an MQTT protocol;
and fifthly, performing fault diagnosis analysis on the received data through a fault diagnosis system intelligent analysis module, making a decision, and returning a decision result to the edge end.
2. The method for constructing the edge of the fault diagnosis system based on the distributed internet of things according to claim 1, wherein the data acquisition module is specifically configured to:
collecting vibration data of a vibration sensor according to a specified frequency;
collecting temperature sensor data according to a specified frequency;
simultaneously connected with a plurality of sensors;
synchronizing time between hosts using a network time protocol, inserting a time stamp into the collected data, and synchronizing with a clock;
serial interface data is collected.
3. The method for constructing an edge of a fault diagnosis system based on the distributed internet of things according to claim 2, wherein the step one of collecting data of the device object based on the factory method mode comprises:
defining abstract product class, namely Sensor interface, which is used for describing the attribute and function of the Sensor, checking the state of the Sensor and initializing the Sensor by connecting with the Sensor, and obtaining Sensor data;
dividing sensor subclasses according to different sensor types and different parameters, checking sensor states in the sensor subclasses, and initializing and collecting data;
defining abstract factory classes for creating different types of sensor instances, namely specific factory classes, according to different parameter requirements; the abstract factory class is packaged with a method for creating different types of devices;
when the sensor data are collected, a method for creating different types of equipment in a specific factory class is called, and a sensor instance is returned to collect the sensor data.
4. The method for constructing the edge of the fault diagnosis system based on the distributed internet of things according to claim 3, wherein the third step comprises:
defining a data type, including the name of the data and the data type;
defining a data format, including a data length and a unit;
defining a data structure comprising attribute information of data, and defining a data acquisition module comprising a plurality of sensors or controllers, wherein acquired data formats comprise vibration, temperature and pressure attributes;
determining a mapping rule, mapping original data into a standardized format according to a mapping data relationship, and mapping sensor data into a row protocol type;
mapping between data models is carried out, and sensor data are converted into a row protocol format and stored in a time sequence database.
5. The method for constructing the edge of the distributed internet of things-based fault diagnosis system according to claim 4, wherein the fourth step comprises:
1) The MQTT gateway module runs an MQTT client and is connected with a cloud MQTT proxy server at the same time, and edge information is in butt joint with the cloud, wherein the edge information comprises an edge equipment ID and a type;
2) The MQTT gateway module distributes the data after secondary processing and the original data to a specific theme;
3) The MQTT proxy server of the cloud end is used as a subscriber to subscribe to a specific topic.
6. The method for constructing the edge of the distributed internet of things-based fault diagnosis system according to claim 5, wherein the fifth step comprises:
verifying the function of an intelligent analysis module of the fault diagnosis system based on offline data, if the function is normal, establishing communication connection, and performing test verification based on data uploaded by the MQTT gateway; if the verification is correct, deploying the intelligent analysis module of the fault diagnosis system at the edge end, constructing the edge end of the fault diagnosis system and connecting the edge end with the sensor, and carrying out overall verification and debugging.
7. The method for constructing the edge of the distributed internet of things-based fault diagnosis system according to claim 5, wherein the fifth step further comprises:
and setting an API (application program interface) in the intelligent analysis module of the fault diagnosis system, and introducing a new algorithm and a corresponding data file, and debugging after the new algorithm and the corresponding data file are introduced.
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